| name | Prediction Market AI Event Analysis |
| description | Information-transfer arbitrage for prediction markets — detect when market price deviates from superior reference price and capture convergence. Not forecasting. |
| version | 1.0 |
| format | completion-engine |
| workflows | 3 |
| source | MES 3.0 Deep Extraction — sovereign2013 + PolySwarm + ensemble bots (11 sources, 607 lines) |
Prediction Market AI Event Analysis
Core Insight
Prediction market trading is NOT a forecasting problem. It is an information-transfer arbitrage problem. The 7.6% of wallets that profit do not predict outcomes better than the market. They detect when the market's price deviates from a superior reference price (sportsbook odds, exchange spot prices, multi-model ensemble consensus) and capture the convergence.
sovereign2013's $1-to-$3.3M run is the proof case: 37,247 bets in ~8 months, almost exclusively sports, Claude-powered. The bot does not "know" sports. It knows that Vegas knows sports, and that Polymarket participants don't know what Vegas knows.
What This Skill Does
Three workflows covering the full prediction market trading pipeline:
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Odds Discrepancy Scanner (workflows/odds-discrepancy-scanner.md) — Scans active Polymarket sports/event markets against sportsbook reference odds. Converts to implied probabilities, identifies gaps exceeding fee drag, outputs ranked opportunity list with confidence levels and action recommendations.
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Edge Validation & Sizing (workflows/edge-validation-sizing.md) — Takes an identified discrepancy through a 6-point validation framework: data integrity, gap explanation, fee-adjusted edge, position sizing (quarter-Kelly), timing check, and kill conditions. Outputs a validated trade plan or rejection with reasons.
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Multi-Model Ensemble (workflows/multi-model-ensemble.md) — Runs independent analysis through 3 weighted perspectives (analytical 40%, credibility 35%, contrarian 25%), aggregates via Bayesian mixture with 30% market weight, outputs probability estimate with confidence, disagreement analysis, and trade recommendation.
Domain Context
| Fact | Number | Source |
|---|
| Profitable wallets | 7.6% of 50,000+ | Wallet analysis |
| Bot dominance | 14 of 20 top wallets | 2026 data |
| Arbitrage window (2024) | 12.3 seconds avg | Market data |
| Arbitrage window (2026) | 2.7 seconds avg | Market data |
| Paper-to-live gap | 522x paper vs -49.5% live | Jung-Hua Liu v2 |
| Annual extraction | ~$40M from Polymarket | Apr 2024-Apr 2025 |
| Human underperformance | ~18% vs bots on same strategy | Behavioral analysis |
Critical Warnings
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The paper-to-live gap is the central risk. Multiply any paper edge by 0.5-0.7 before committing capital. If it doesn't survive the haircut, it isn't real.
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Execution is 70% of success. Slippage (2-4 cents), fees (0.75-2%), latency, and liquidity collectively destroy edges that look massive on paper.
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Start with sports arbitrage, not crypto. Sports have the best reference prices (sportsbook odds), the widest Polymarket inefficiency, and don't require sub-100ms infrastructure.
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92.4% of wallets are unprofitable. The three killers: oversized positions, late entries, inconsistent risk management.
Expert Weights (Multi-Model Ensemble)
| Model | Weight | Strength |
|---|
| GPT-4o | 40% | Broad analytical reasoning, statistical pattern recognition |
| Claude | 35% | Source credibility evaluation, nuanced uncertainty reasoning |
| Gemini | 25% | Contrarian perspective, alternative data interpretation |
Position Sizing Formula
Edge = (Your_Probability - Market_Price) / (1 - Market_Price)
Position = 0.25 * Edge * Bankroll
Cap at min(Position, 0.05 * Bankroll)
Hard caps: 5% single position, 15% correlated exposure, 5% daily drawdown circuit breaker.
Market Segmentation
| Segment | Speed Required | Reference Price | Who Wins |
|---|
| Ultra-short crypto (5-15 min) | Sub-100ms | Exchange spot | Infrastructure bots only |
| Sports events | Seconds | Sportsbook odds | Claude-speed bots |
| Political/economic | Minutes | Polling/models | AI ensembles + judgment |
When to Use Each Workflow
- Known sportsbook odds + active Polymarket markets -> Odds Discrepancy Scanner
- Identified gap needs validation before capital deployment -> Edge Validation & Sizing
- Complex event market without clean reference price -> Multi-Model Ensemble
- Full pipeline: Scanner -> Ensemble (if scanner flags INVESTIGATE) -> Edge Validation -> Execute/Pass
Genius File
genius.md contains: 12 Genius Patterns, 10 Hidden Knowledge items, 3 Exemplars + 1 Anti-Exemplar, 7 Signature Moves, and the Quality Rubric. Load for deep analysis, complex market evaluation, or when output quality needs to exceed score 7.
Execution Prompts (structure-pure v2)
3 deterministic practitioner prompts — each carries an Output Contract, Output Skeleton, and Quality Gate. When a deliverable matches one, Read it and honor its contract instead of improvising the output shape.
- Prediction Market Analyst — Edge Validation & Trade Plan —
skills/prediction-market-ai-event-analysis/references/prompts-v2/edge-validation-trade-plan.md
- Prediction Market Analyst — Multi-Model Ensemble Forecast —
skills/prediction-market-ai-event-analysis/references/prompts-v2/multi-model-ensemble-forecast.md
- Prediction Market Analyst — Odds Discrepancy Scan —
skills/prediction-market-ai-event-analysis/references/prompts-v2/odds-discrepancy-scan.md
